Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates

Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.

[1]  Trupti Joshi,et al.  Inferring gene regulatory networks from multiple microarray datasets , 2006, Bioinform..

[2]  David J. John,et al.  Bayesian probabilistic network modeling from multiple independent replicates , 2012, BMC Bioinformatics.

[3]  Jacquelyn S. Fetrow,et al.  Continuous Cotemporal Probabilistic Modeling of Systems Biology Networks from Sparse Data , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  Han Woo Lee,et al.  Genome-wide analysis of the auxin-responsive transcriptome downstream of iaa1 and its expression analysis reveal the diversity and complexity of auxin-regulated gene expression , 2009, Journal of experimental botany.

[5]  H. Bolouri.  Computational Modeling of Gene Regulatory Networks - A Primer , 2008 .

[6]  Lei Ding,et al.  Arabidopsis extra-large G proteins (XLGs) regulate root morphogenesis. , 2007, The Plant journal : for cell and molecular biology.

[7]  Godelieve Gheysen,et al.  Transcription factor WRKY23 assists auxin distribution patterns during Arabidopsis root development through local control on flavonol biosynthesis , 2012, Proceedings of the National Academy of Sciences.

[8]  D. Weijers,et al.  The PINOID protein kinase regulates organ development in Arabidopsis by enhancing polar auxin transport. , 2001, Development.

[9]  Edward E Allen,et al.  Algebraic dependency models of protein signal transduction networks from time-series data. , 2006, Journal of theoretical biology.

[10]  Brandilyn Stigler,et al.  A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development , 2012, BMC Systems Biology.

[11]  Kengo Kinoshita,et al.  ATTED-II provides coexpressed gene networks for Arabidopsis , 2008, Nucleic Acids Res..

[12]  E. Liscum,et al.  Genetics of Aux/IAA and ARF action in plant growth and development , 2002, Plant Molecular Biology.

[13]  Tin Wee Tan,et al.  Large-scale analysis of antigenic diversity of T-cell epitopes in dengue virus , 2006, BMC Bioinformatics.

[14]  D. Pe’er.  Bayesian Network Analysis of Signaling Networks: A Primer , 2005, Science's STKE.

[15]  J. Reed,et al.  Roles and activities of Aux/IAA proteins in Arabidopsis. , 2001, Trends in plant science.

[16]  Hongzhe Li,et al.  Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks. , 2006, Biostatistics.

[17]  G. Muday,et al.  PINOID Kinase Regulates Root Gravitropism through Modulation of PIN2-Dependent Basipetal Auxin Transport in Arabidopsis1[W][OA] , 2009, Plant Physiology.

[18]  Anne-Laure Boulesteix,et al.  Regularized estimation of large-scale gene association networks using graphical Gaussian models , 2009, BMC Bioinformatics.

[19]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[20]  Jie Han,et al.  Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks , 2012, BMC Systems Biology.

[21]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[22]  Jacquelyn S Fetrow,et al.  A Kinetic Analysis of the Auxin Transcriptome Reveals Cell Wall Remodeling Proteins That Modulate Lateral Root Development in Arabidopsis[W][OPEN] , 2013, Plant Cell.

[23]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[24]  Joseph Hilbe,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .

[25]  Hamid Bolouri,et al.  A data integration methodology for systems biology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  P. Zimmermann,et al.  GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox1[w] , 2004, Plant Physiology.

[27]  J. Reed,et al.  Arabidopsis SHY2/IAA3 Inhibits Auxin-Regulated Gene Expression Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.010283. , 2002, The Plant Cell Online.

[28]  N. D. Clarke,et al.  Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PloS one.

[29]  A. Theologis,et al.  ARF7 and ARF19 Regulate Lateral Root Formation via Direct Activation of LBD/ASL Genes in Arabidopsis[W] , 2007, The Plant Cell Online.

[30]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[31]  James S. Clark,et al.  Tree growth inference and prediction from diameter censuses and ring widths. , 2007, Ecological applications : a publication of the Ecological Society of America.

[32]  Kristopher L. Patton.  Bayesian interaction and association networks from multiple replicates of sparse time-course data , 2012 .

[33]  O. Leyser,et al.  Changes in auxin response from mutations in an AUX/IAA gene. , 1998, Science.

[34]  Jiguo Cao,et al.  Modeling gene regulation networks using ordinary differential equations. , 2012, Methods in molecular biology.

[35]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[36]  Peter D. Hoff,et al.  A First Course in Bayesian Statistical Methods , 2009 .

[37]  Ting Chen,et al.  An integrated approach to the prediction of domain-domain interactions , 2006, BMC Bioinformatics.

[38]  Alberto de la Fuente,et al.  Discovery of meaningful associations in genomic data using partial correlation coefficients , 2004, Bioinform..

[39]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[40]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[41]  R. Laubenbacher,et al.  A computational algebra approach to the reverse engineering of gene regulatory networks. , 2003, Journal of theoretical biology.

[42]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[43]  David J. John,et al.  Probabilistic Signal Network Models from Multiple Replicates of Sparse Time-Course Data , 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine.

[44]  P. Bühlmann,et al.  Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana , 2004, Genome Biology.

[45]  Ravinder Singh,et al.  Fast-Find: A novel computational approach to analyzing combinatorial motifs , 2006, BMC Bioinformatics.